Do you often question the accuracy of the data coming out of your products? Does your product development seem slow because too much time is spent fixing data issues? This common problem is rooted in what we call the "data creation tax" that product engineering teams are forced to pay. Let's explore the impact of this tax and how we can address it.

The Real-World Impact of Data Issues

Imagine discovering that user engagement metrics for a popular app were undercounted due to a bug that prevented all analytics events from being sent. This issue was spotted by the engineering team while reviewing daily metrics. For days, the metrics had to be "grayed out" in reports until an update was released. This not only led to mistrust in the data but also slowed down product-related decision-making.

This scenario is all too common for product teams, who frequently grapple with questions such as:

• Is a drop in engagement due to organic reasons or a measurement error?
• Are poor recommendations due to faulty ML models or incorrect data inputs?
• Are product changes driving positive impacts across all customer segments?

The Widespread Effects of Data Issues

Product usage data is essential for various functions, from experimentation to operational analytics. Poor data quality can have far-reaching impacts:

• Product and Growth Teams: These teams rely on accurate data for rapid experimentation and A/B testing. Data issues can invalidate these tests and hinder innovation.
• ML-Driven Features: Real-time data is crucial for features like recommendations. Bad data can negatively affect user experience and revenue within seconds.
• Operational Analytics: Sales, marketing, finance, and customer success teams depend on accurate data to measure ROI and drive growth. Poor data quality can lead to incorrect business decisions and increased risk.

Addressing Data Creation at the Source

The root cause of these data issues often lies in the data creation process. Incomplete or inaccurate source data is a critical problem because it can't be fixed downstream. Product teams are usually focused on shipping features quickly, with data instrumentation often being an afterthought. This results in incomplete or incorrect data being collected, which affects all downstream processes.

The Data Creation Tax

The "data creation tax" refers to the extra time and effort required to manage and correct data issues. Several factors contribute to this tax:

• Development Priorities: Developers focus on building and testing features, with data instrumentation often added later.
• Unknown Data Needs: Teams may not fully understand which data they need until after testing the product.
• Coordination Challenges: Implementing telemetry requires coordination across multiple teams, which can slow down the process.

The Way Forward

To eliminate the data creation tax, we need better tools, not just processes. These tools should help product teams efficiently collect trustworthy data without adding extra burden. This involves:

• Accurate Data Capture: Ensuring data is captured correctly at the source.
• Improved Tooling: Providing developers with tools to model, test, and maintain data events as easily as they do product features.
• Integrated Approaches: Combining the efforts of product, engineering, data, privacy, and legal teams to streamline data creation.

Achieving Completeness and Correctness

Data completeness and correctness are critical for reliable analytics:

• Completeness: Capturing all necessary data points, such as every step in a checkout funnel, ensures comprehensive analysis.
• Correctness: Ensuring data accurately reflects reality, such as correctly logging all order placements, avoids misleading conclusions.

Inadequate data at the source leads to significant downstream issues:

• Product Innovation Stalls: Without accurate data, evaluating experiments becomes difficult, delaying new product features.
• Operational Inefficiencies: Sales, marketing, and finance teams struggle to measure ROI and make informed decisions.
• Inaccurate ML Models: Machine learning models rely on high-quality data, and poor data collection hampers their effectiveness.

Overcoming the Data Deficit

A "data deficit" occurs when teams lack the necessary data or can't trust the data they have. This deficit arises from the complex, error-prone data creation process. To overcome this, consider:

• Streamlining Data Creation: Simplify the process with tools that ensure accurate and complete data capture from the start.
• Empowering Product Teams: Equip teams with the resources to model, test, and maintain data instrumentation seamlessly.
• Fostering Cross-Functional Collaboration: Encourage coordination between product, engineering, data, privacy, and legal teams to enhance data quality.

Conclusion

By addressing data issues at the source and providing better tools for data creation, product teams can reduce the data creation tax. This will enable faster product iteration, more accurate decision-making, and ultimately, a more agile and responsive organization.

Call to Action

If you relate to these challenges and are looking for ways to improve your data creation process, consider exploring new tools and approaches to help your teams work more efficiently and effectively.